Learning behaviors of stochastic automata under multi-teacher environment

AbstractStochastic Automata Networks (SANs) have a large amount of applications in modelling queueing systems and communication systems. To find the steady state probability distribution of the SANs, it often needs to solve linear systems which involve their generator matrices. However, some classical iterative methods such as the Jacobi and the Gauss-Seidel are inefficient due to the huge size of the generator matrices. In this paper, the multipreconditioned GMRES (MPGMRES) is considered by using two or more preconditioners simultaneously. Meanwhile, a selective version of the MPGMRES is presented to overcome the rapid increase of the storage requirements and make it practical. Numerical results on two models of SANs are reported to illustrate the effectiveness of these proposed methods.

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Team Learning Behaviors and Performance: A Meta-Analysis of Direct Effects and Moderators

Group & Organization Management ◽

10.1177/10596011211016928 ◽

2021 ◽

pp. 105960112110169

Author(s):

Christopher W. Wiese ◽

C. Shawn Burke ◽

Yichen Tang ◽

Claudia Hernandez ◽

Ryan Howell

Keyword(s):

Team Performance ◽

Team Learning ◽

Task Complexity ◽

Meta Analysis ◽

Sample Type ◽

Learning Behaviors ◽

Learning Teams ◽

And Performance ◽

The Relationship ◽

Team Familiarity

Under what conditions do team learning behaviors best predict team performance? The current meta-analytic efforts synthesize results from 113 effect sizes and 7758 teams to investigate how different conceptualizations (fundamental, intrateam, and interteam), team characteristics (team size and team familiarity), task characteristics (interdependence, complexity, and type), and methodological characteristics (students vs. nonstudents and measurement choice) affect the relationship between team learning behaviors and team performance. Our results suggest that while different conceptualizations of team learning behaviors independently predict performance, only intrateam learning behaviors uniquely predict performance. A more in-depth investigation into the moderating conditions contradicts the familiar adage of “it depends.” The strength of the relationship between intrateam learning behaviors and team performance did not depend on team familiarity, task complexity, or sample type. However, our results suggested this relationship was stronger in larger teams, teams with moderate task interdependence, teams performing project/action tasks, and studies that use measures that capture a wider breadth of the team learning behavior construct space. These efforts suggest that common boundary conditions do not moderate this relationship. Scholars can leverage these results to develop more comprehensive theories addressing the different conceptualizations of team learning behaviors as well as providing clarity on the scenarios where team learning behaviors are most needed. Further, practitioners can use our results to develop more guided team-based policies that can overcome some of the challenges of forming and developing learning teams.

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A Resource Model of Team Resilience Capacity and Learning

Group & Organization Management ◽

10.1177/10596011211018008 ◽

2021 ◽

pp. 105960112110180

Author(s):

Kyle M. Brykman ◽

Danielle D. King

Keyword(s):

Goal Orientation ◽

Team Learning ◽

Empirical Support ◽

Business Environment ◽

Theory And Practice ◽

Conservation Of Resources ◽

Learning Behaviors ◽

Team Resilience ◽

Advance Theory ◽

Resilience Capacity

A team’s capacity to bounce back from adversities or setbacks (i.e., team resilience capacity) is increasingly valuable in today’s complex business environment. To enhance our understanding of the antecedents and consequences of team resilience capacity, we develop and empirically test a resource-based model that delineates critical team inputs and outputs of resilience capacity. Drawing from conservation of resources theory, we propose that voice climate is a critical resource that builds team resilience capacity by encouraging intrateam communication and that leader learning goal orientation (LGO) amplifies this relationship by orienting team discourse toward understanding and growing from challenges. In turn, we propose that team resilience capacity is positively related to team learning behaviors, as teams with a higher resilience capacity are well-positioned to invest their resources into learning activities, and that team information elaboration amplifies this relationship by facilitating resource exchange. Results of a time-lagged, multisource field study involving 48 teams from five Canadian technology start-ups supported this moderated-mediated model. Specifically, voice climate was positively related to team resilience capacity, with leader LGO amplifying this effect. Further, team resilience capacity was positively related to team learning behaviors, with information elaboration amplifying this effect. Altogether, we advance theory and practice on team resilience by offering empirical support on what builds team resilience capacity (voice climate) and what teams with a high resilience capacity do (learning), along with the conditions under which these relationships are enhanced (higher leader LGO and team information elaboration).

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